Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning

Abstract

Background: We sought to test the hypothesis that transcriptiome-level genes signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who later were clinically classified as lithium treatment responders. Methods: Gene expression study data was obtained from the Lithium Treatment-Moderate dose Use Study data accessed from the National Center for Biotechnology Information’s Gene Expression Omnibus via accession number GSE4548. Differential gene expression analysis was conducted using the Linear Models for Microarray and RNA-Seq (limma) package and the Random Forests machine learning algorithm in R. Results: In pre-treatment lithium responders, the following genes were found having a greater than 0.5 fold-change, and differentially expressed indicating a male bias: RBPMS2, SIDT2, CDH23, LILRA5, and KIR2DS5; while the female-biased genes were: HLA-H, RPS23, FHL3, RPL10A, NBPF14, PSTPIP2, FAM117B, CHST7, and ABRACL. Conclusions: Using machine learning, we developed a pre-treatment gender- and gene-expression-based predictive model selective for lithium responders with an ROC AUC of 0.92 for men and an ROC AUC of 1 for women.